A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization. (arXiv:2208.06125v1 [cs.LG])
Latent Factor (LF) models are effective in representing high-dimension and
sparse (HiDS) data via low-rank matrices approximation. Hessian-free (HF)
optimization is an efficient method to utilizing second-order information of an
LF model's objective function and it has been utilized to optimize second-order
LF (SLF) model. However, the low-rank representation ability of a SLF model
heavily relies on its multiple hyperparameters. Determining these
hyperparameters is time-consuming and it largely reduces the practicability of
an SLF model. To address this issue, a practical SLF (PSLF) model is proposed
in this work. It realizes hyperparameter self-adaptation with a distributed
particle swarm optimizer (DPSO), which is gradient-free and parallelized.
Experiments on real HiDS data sets indicate that PSLF model has a competitive
advantage over state-of-the-art models in data representation ability.
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